code: def get_split(self, k, repeat=0):
      repeat_str = f"_repeat_{repeat}" if self.repeats > 1 else ""
  
      train_index_file = os.path.join(
          self._results_path,
          "folds",
          f"fold_{k}{repeat_str}_train_indices.npy",
      )
      validation_index_file = os.path.join(
          self._results_path,
          "folds",
          f"fold_{k}{repeat_str}_validation_indices.npy",
      )
  
      train_index = np.load(train_index_file)
      validation_index = np.load(validation_index_file)
  
      X = load_data(self._X_path)
      y = load_data(self._y_path)
      y = y["target"]
  
      sample_weight = None
      if self._sample_weight_path is not None:
          sample_weight = load_data(self._sample_weight_path)
          sample_weight = sample_weight["sample_weight"]
  
      sensitive_features = None
      if self._sensitive_features_path is not None:
          sensitive_features = load_data(self._sensitive_features_path)
  
      train_data = {"X": X.loc[train_index], "y": y.loc[train_index]}
      validation_data = {
          "X": X.loc[validation_index],
          "y": y.loc[validation_index],
      }
      if sample_weight is not None:
          train_data["sample_weight"] = sample_weight.loc[train_index]
          validation_data["sample_weight"] = sample_weight.loc[
              validation_index
          ]
  
      if sensitive_features is not None:
          train_data["sensitive_features"] = sensitive_features.loc[
              train_index
          ]
          validation_data[
              "sensitive_features"
          ] = sensitive_features.loc[validation_index]
  
      return (train_data, validation_data)
  
file_path: mljar-supervised/supervised/validation/validator_kfold.py
imports: import gc
  import logging
  import os
  import warnings
  import numpy
  import pandas
  import time
  from sklearn.model_selection import KFold, StratifiedKFold
  from supervised.exceptions import AutoMLException
  from supervised.utils.config import mem
  from supervised.utils.utils import load_data
  from supervised.validation.validator_base import BaseValidator
label_desc: Use image node_id 2 for calling the KFoldValidator obj's underlying member method code with example usage: obj.get_split(k, repeat) and returns: train_data,  validation_data